Process (computing)

Statistics

Accounting

Operating system

Selection (genetic algorithm)

Data mining

Computer science

Business

Variance (accounting)

Mathematics

Estimator

Artificial intelligence

Bias of an estimator

Minimum-variance unbiased estimator

Recommender system

Machine learning

Recommendation datasets are prone to selection biases due to self-selection behavior of users and item selection process of systems. This makes explicitly combating selection biases an essential problem in training recommender systems. Most previous studies assume no unbiased data available for training. We relax this assumption and assume that a small subset of training data is unbiased. Then, we propose a novel objective that utilizes the unbiased data to adaptively assign propensity weights to biased training ratings. This objective, combined with unbiased performance estimators, alleviates the effects of selection biases on the training of recommender systems. To optimize the objective, we propose an efficient algorithm that minimizes the variance of propensity estimates for better generalized recommender systems. Extensive experiments on two real-world datasets confirm the advantages of our approach in significantly reducing both the error of rating prediction and the variance of propensity estimation.

Combating Selection Biases in Recommender Systems with a Few Unbiased Ratings